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Positron Emission Tomography/Computed Tomography (PET/CT) combines metabolic and anatomical information improving the precision and accuracy of oncological diagnostics. The standardized uptake value (SUV) measures tumor metabolism, yet its accuracy is influenced by the partial volume effect (PVE), impacting small lesion detection. This study aims to refine PVE corrections for small lesions using an in-house customized, special anthropomorphic phantom. Scans of this phantom which contained spheres of different sizes were performed across four hospitals at different PET/CT systems from various manufacturers (Siemens and Philips analog PET/CT systems, GE analog and digital PET/CT systems). The phantom contained six custom-designed cylinders with embedded spheres simulating sub-centimeter (0.3, 0.5, 0.9) and centimeter (1.3, 1.9, 2.8) lesions. Scans were performed separately for each sphere in the thorax, abdomen, and pelvis regions at all sites. Recovery Coefficients (RCs) were calculated to correct SUV values, demonstrating that RCs vary by sphere size and anatomical region but not change significantly among scanners. RCs are approaching unity for larger spheres, ensuring accurate SUV measurements. However, small spheres (< 0.5 cm) exhibited significant measurement challenges due to PVE. The anthropomorphic phantom proved effective in obtaining realistic SUV-corrected values, offering a promising tool for enhancing the accuracy and standardization of PET imaging in oncology. This study underscores the necessity for advanced imaging technologies and standardized RC application in clinical settings to improve the quantification of PET imaging, particularly in small lesion detection.

A voxel-based algorithm to correct for partial volume effect in PET brain volumes is presented. This method (named LoReAn) is based on MRI based segmentation of anatomical regions and accurate measurements of the effective point spread function of the PET imaging process. The objective is to correct for the spill-out of activity from high-uptake anatomical structures (e.g. grey matter) into low-uptake anatomical structures (e.g. white matter) in order to quantify physiological uptake in the white matter. The new algorithm is presented and validated against the state of the art region-based geometric transfer matrix (GTM) method with synthetic and clinical data. Using synthetic data, both bias and coefficient of variation were improved in the white matter region using LoReAn compared to GTM. An increased number of anatomical regions doesn't affect the bias (<5%) and misregistration affects equally LoReAn and GTM algorithms. The LoReAn algorithm appears to be a simple and promising voxel-based algorithm for studying metabolism in white matter regions. [Display omitted] ► Voxel-based intensity diffusion correction algorithm for PET volumes is presented. ► Algorithm relies on accurate measurement of the PSF. ► Estimates the true uptake by local regression analysis. ► Improves bias and CoV in WM structures compared to the GTM method. ► The use of the algorithm reduces intrinsic correlation between GM and WM uptake.

A voxel-based algorithm to correct for partial volume effect in PET brain volumes is presented. This method (named LoReAn) is based on MRI based segmentation of anatomical regions and accurate measurements of the effective point spread function of the PET imaging process. The objective is to correct for the spill-out of activity from high-uptake anatomical structures (e.g. grey matter) into low-uptake anatomical structures (e.g. white matter) in order to quantify physiological uptake in the white matter. The new algorithm is presented and validated against the state of the art region-based geometric transfer matrix (GTM) method with synthetic and clinical data. Using synthetic data, both bias and coefficient of variation were improved in the white matter region using LoReAn compared to GTM. An increased number of anatomical regions doesn't affect the bias (<5%) and misregistration affects equally LoReAn and GTM algorithms. The LoReAn algorithm appears to be a simple and promising voxel-based algorithm for studying metabolism in white matter regions.

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

Objective(s):Somatostatin receptor scintigraphy (SRS) using 111In-pentetreotide has no established quantification method. The purpose of this study was to develop a new quantitative method to correct the partial volume effect (PVE) for individual energy peaks in 111In-pentetreotide single-photon emission computed tomography (SPECT).Methods:Phantom experiments were performed to construct a new quantitative method. In the phantom experiments, a NEMA IEC body phantom was used. Acquisition was performed using two energy peaks (171 keV and 245 keV) on the SPECT/CT system. The volume of interest was set at each hot sphere and lung insert in the SPECT images of each energy peak, and the recovery coefficient (RC) was calculated to understand the PVE. A new quantitative index, the indium uptake index (IUI), was calculated using the RC to correct the PVE. The quantitative accuracy of the IUI in the hot sphere was confirmed. Case studies were performed to clarify the quantitative accuracy. In a case study, the relationship between the IUI and the Krenning score, which is used as a visual assessment, was evaluated for each lesion.Results:The obtained RCs showed that the energy peak at 171 keV was faster in recovering the effect of PVE than that at 245 keV. The IUI in the 17-mm-diameter hot sphere was overestimated by 4.8% and 8.3% at 171 keV and 245 keV, respectively, compared to the actual IUIs. The relationship between IUI and Krenning score was rs=0.773 (p<0.005) at sum, rs=0.739 (p<0.005) at 171 keV, and rs=0.773 (p<0.005) at 245 keV.Conclusion: We have developed a new quantification method for 111In-pentetreotide SPECT/CT using RC-based PVE correction for an individual energy peak of 171 keV. The quantitative accuracy of this method was high even for accumulations of less than 20 mm, and it showed a good relationship with the Krenning score; therefore, the clinical usefulness of IUI was demonstrated.

Background: Diagnosis and timely treatment of ischemic stroke depends on the fast and accurate quantification of perfusion parameters. Arterial input function (AIF) describes contrast agent concentration over time as it enters the brain through the brain feeding artery. AIF is the central quantity required to estimate perfusion parameters. Inaccurate and distorted AIF, due to partial volume effects (PVE), would lead to inaccurate quantification of perfusion parameters. Methods: Fifteen patients suffering from stroke underwent perfusion MRI imaging at the Tri-Service General Hospital, Taipei. Various degrees of the PVE were induced on the AIF and subsequently corrected using rescaling methods. Results: Rescaled AIFs match the exact reference AIF curve either at peak height or at tail. Inaccurate estimation of CBF values estimated from non-rescaled AIFs increase with increasing PVE. Rescaling of the AIF using all three approaches resulted in reduced deviation of CBF values from the reference CBF values. In most cases, CBF map generated by rescaled AIF approaches show increased CBF and Tmax values on the slices in the left and right hemispheres. Conclusion: Rescaling AIF by VOF approach seems to be a robust and adaptable approach for correction of the PVE-affected multivoxel AIF. Utilizing an AIF scaling approach leads to more reasonable absolute perfusion parameter values, represented by the increased mean CBF/Tmax values and CBF/Tmax images.